Concurrency in .NET
Modern patterns of concurrent and parallel programming
Concurrency in .NET
Modern patterns of concurrent and parallel programming
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Slideshare: Embracing Concurrent Programming With Functional
The Foundations of Functional Concurrency
Article: The Parallel Functional Pipeline Pattern
Source code on GitHub
Article: Parallel K-Means Clustering with reducer function
Article: Parallel K-Means Clustering with reducer function
Article: Parallelizing Tasks with dependencies — Design your code to optimize performance
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A complementary source of knowledge about modern concurrent functional programming on the .NET platform—an absolute must-read.
Concurrency in .NET teaches you how to build concurrent and scalable programs in .NET using the functional paradigm. This intermediate-level guide is aimed at developers, architects, and passionate computer programmers who are interested in writing code with improved speed and effectiveness by adopting a declarative and pain-free programming style.
Table of Contents takes you straight to the bookdetailed table of contents
Part 1: Functional Concurrent programming Concepts
1. Functional Concurrency Foundations
1.1. What you will learn from this book
1.2. Let’s start with terminology
1.2.1. Sequential programming performs one task at a time
1.2.2. Parallel programming executes multiples tasks simultaneously
1.2.3. Multitasking performs multiple tasks concurrently over time
1.2.4. Multithreading for performance improvement
1.3. Why the need for concurrency?
1.3.1. Present and future of concurrent programming
1.4. The pitfalls of concurrent programming
1.4.1. Concurrency hazards
1.4.2. The sharing of state evolution
1.4.3. A simple real-world example: parallel quicksort
1.4.4. Benchmarking in F#
1.5. Why choose functional programming for concurrency
1.5.1. Benefits of functional programming
1.6. Embracing the functional paradigm
1.7. Why use F# and C# for functional concurrent programming
1.8. Summary
2. Functional Programming Techniques for Concurrency
2.1. Using function composition to solve complex problems with simple solutions
2.1.1. Function composition in C#
2.1.2. Function composition in F#
2.2. Closure to simplify functional thinking
2.2.1. Captured variables in closures with lambda expressions
2.2.2. Closure in a multithreading environment
2.2.3. Memoization-caching technique for program speedup
2.2.4. Memoize in action for a fast Web-Crawler
2.2.5. Lazy memoization for better performance
2.2.6. Gotchas for function memoization
2.3. Effective concurrent speculation to amortize the cost of expensive computations
2.3.1. Precomputation with natural functional support
2.3.2. Let the best computation win
2.4. Being lazy is a good thing
2.4.1. Strict programming languages for better understanding concurrent behaviors
2.4.2. Lazy caching technique and thread-safe singleton pattern
2.4.3. Lazy support in F#
2.4.4. Lazy and Task, a powerful combination
2.5. Summary
3. Functional Data Structures and Immutability
3.1. Real-world example: Hunt the thread-unsafe object
3.1.1. .NET immutable collections: a safe solution
3.1.2. The .NET concurrent collections: a faster solution
3.1.3. The Agent message passing pattern: a faster and better solution
3.2. Functional data structures can be shared safely among threads
3.3. Immutability for a change
3.3.1. Functional data structure for data parallelism
3.3.2. Performance implication behind using immutability
3.3.3. Immutability in C#
3.3.4. Immutability in F#
3.3.5. Functional lists - linking cells in a chain
3.3.6. Building a persistent data structure: an immutable binary tree (B-Tree)
3.4. Recursive function: a natural way to iterate
3.4.1. The tail of a correct recursive function: Tail-Call optimized
3.4.2. Continuation passing style (CPS) to optimize recursive function
3.5. Summary
Part 2 How to approach different parts of a concurrent program
4. The Basics of Processing Big Data: Data Parallelism Part 1
4.1. What is data parallelism
4.1.1. Data and task parallelism
4.1.2. The "embarrassingly parallel" concept
4.1.3. Data parallelism support in .NET
4.2. The Fork/Join pattern: Parallel Mandelbrot
4.2.1. When the garbage collector is the bottleneck: struct vs. class objects
4.2.2. The downside of parallel loops
4.3. How to measure performance speed
4.3.1. The Amdahl’s Law defines the limit of performance improvement
4.3.2. The Gustafson’s Law - a step further to measure performance improvement
4.3.3. The limitations of parallel loops: the sum of prime numbers
4.3.4. What can possibly go wrong with a simple loop?
4.3.5. The declarative parallel programming model
4.4. Summary
5. PLINQ and Map-Reduce: Data Parallelism Part 2
5.1. A short introduction to PLINQ
5.1.1. How is PLINQ more functional?
5.1.2. PLINQ and pure functions: the parallel words counter
5.1.3. Avoiding side effects with pure functions
5.1.4. Isolate and control side effects: refactoring the parallel words counter
5.2. Aggregating and reducing data in parallel
5.2.1. Deforesting: one of many advantages to folding
5.2.2. Fold in PLINQ: the Aggregate functions
5.2.3. Implementing a parallel Reduce function for PLINQ
5.2.4. Parallel list comprehension in F#: PSeq
5.2.5. Parallel array in F#
5.3. Parallel MapReduce pattern
5.3.1. The Map and Reduce functions
5.3.2. MapReduce the NuGet package gallery
5.4. Summary
6. Real-Time Event Streams: Functional Reactive Programming
6.1. What is Reactive programming: Big Event processing
6.2. .NET tools for Reactive programming
6.2.1. Event combinators: a better solution
6.2.2. .NET interoperability with F# combinators
6.3. Reactive programming in .NET: Reactive Extensions (Rx)
6.3.1. From LINQ/PLINQ to Reactive Extensions
6.3.2. IObservable?the dual IEnumerable
6.3.3. Reactive Extensions in action
6.3.4. Real-time streaming with Reactive Extensions
6.3.5. From events to F# observables
6.4. Taming the event stream: Twitter emotion analysis using Reactive Extensions programming
6.4.1. SelectMany: the monadic bind operator
6.5. An Rx publisher-subscriber
6.5.1. The Subject type for a powerful Publisher-Subscriber hub
6.5.2. Reactive Extensions in relation to concurrency
6.5.3. Implementing a reusable Rx Publisher-Subscriber
6.5.4. Analyzing tweet emotions using an Rx Pub-Sub class
6.5.5. Observer in action
6.5.6. The convenient F# object expression
6.6. Summary
7. Task-Based Functional Parallelism
7.1. A short introduction to task parallelism
7.1.1. Why task parallelism and functional programming?
7.1.2. Task parallelism support in .NET
7.2. The .NET Task Parallel Library (TPL)
7.2.1. Running operations in parallel with TPL Parallel.Invoke
7.3. The problem of void in C#
7.3.1. The solution of void in C# - the Unit type
7.4. Continuation-passing style: a functional control flow
7.4.1. Why exploit CPS?
7.4.2. Waiting for a task to complete: the continuation model
7.5. Strategies for composing task operations
7.5.1. Mathematical pattern for better composition
7.5.2. Guidelines for using tasks
7.6. The parallel functional pipeline pattern
7.7. Summary
8. Task Asynchronicity for the win
8.1. The Asynchronous Programming Model (APM)
8.1.1. The value of asynchronous programming
8.1.2. Scalability and Asynchronous Programming
8.1.3. CPU-bound and I/O-bound operations
8.2. Unbounded parallelism with asynchronous programming
8.3. Asynchronous support in .NET
8.3.1. Asynchronous programming breaks the code structure
8.3.2. Event-based asynchronous programming
8.4. The C# Task-Asynchronous-Programming (Async/Await)
8.4.1. Anonymous asynchronous lambdas
8.4.2. Task<T> is a monadic container
8.5. TAP Asynchronous processing: a case study
8.5.1. Asynchronous cancellation
8.5.2. Task-based asynchronous composition with the monadic Bind operator
8.5.3. Deferring asynchronous computation enables composition
8.5.4. Retry if something goes wrong
8.5.5. Handling errors in asynchronous operations
8.5.6. Asynchronous parallel processing of historical Stock Market
8.5.7. Asynchronous stock market parallel process as tasks complete
8.6. Summary
9. Asynchronous functional programming in F#
9.1. Asynchronous functional aspect
9.2. What is the F# Asynchronous-Workflow
9.2.1. The continuation passing style in computation expressions
9.2.2. The asynchronous workflow in action: Azure Blob storage parallel operations
9.3. Asynchronous Computation Expressions
9.3.1. Difference between computation expressions and monads
9.3.2. AsyncRetry: building your own computation expression
9.3.3. Extending the asynchronous workflow
9.3.4. Mapping asynchronous operation: The Async.map Functor
9.3.5. Parallelize asynchronous workflows: Async.Parallel
9.3.6. Asynchronous workflow cancellation support
9.3.7. Taming parallel asynchronous operations
9.4. Summary
10. Functional combinators for fluent concurrent programming
10.1. The execution flow is not always on the "happy path": Error handling
10.1.1. The problem of error handling in imperative programming
10.1.2. Errors Combinators: Retry, Fallback, Otherwise, and Task.Catch in C#
10.2. Error handling in functional programming: exceptions for flow control
10.3. Handling errors with the Task<Option<T>> type in C#
10.4. The AsyncOption type in F#: Combining the Async and Option types
10.5. Idiomatic F# functional asynchronous error handling: Async.Catch
10.6. Preserving exception semantic with the Result type
10.7. The composite Task<Result<T>> type: taming exceptions in asynchronous operations
10.8. Modelling error handling in F# with the Async and Result types
10.9. Extending the F# AsyncResult type with monadic bind operators
10.9.1. Abstracting operations with functional combinators
10.9.2. Functional combinators in the nutshell
10.10. The Task Parallel Library build in asynchronous combinators
10.11. Exploiting the Task.WhenAny combinator for redundancy and interleaving
10.12. Exploiting the Task.WhenAll combinator for asynchronous-ForEach
10.13. Mathematical pattern review: Brief analysis of what you have seen so far
10.14. Monoids for data parallelism
10.15. Functors to map elevated types
10.16. Monads to compose without side effects
10.16.1. The ultimate parallel composition: Applicative Functor
10.17. Extending the F# async workflow with applicative functor operators
10.18. Applicative functor semantic in F# with infix operators
10.19. Exploiting heterogeneous parallel computation with Applicative
10.20. Composing and executing heterogeneous parallel computations
10.21. Controllig the flow with conditional asynchronous combinators
10.22. Asynchronous combinators in action
10.23. Summary
11. Applying reactive programming everywhere with Agents
11.1. What is reactive programming, and how is it useful?
11.2. The asynchronous message-passing programming model
11.2.1. Relation with message passing and immutability
11.2.2. Natural isolation
11.3. What is an agent?
11.3.1. The components of an agent
11.3.2. What an agent can do
11.3.3. The share-nothing approach for lock-free concurrent programming
11.3.4. How is agent-based programming functional?
11.3.5. Agent is object-oriented
11.4. The F# agent: MailboxProcessor
11.4.1. The mailbox asynchronous recursive loop
11.5. Avoiding database bottlenecks with F# MailboxProcessor
11.5.1. The MailboxProcessor message type: discriminated unions
11.5.2. The MailboxProcessor two-way communication
11.5.3. Consuming the AgentSQL from C#
11.5.4. Parallelize the workflow with a group-coordination of agents
11.5.5. How to handle errors with F# MailboxProcessor
11.5.6. Stop, cancel, and dispose MailboxProcessor: CancellationToken
11.5.7. Distributing the work with MailboxProcessor
11.5.8. Caching operations with an agent
11.5.9. Reporting results from a MailboxProcessor
11.5.10. Using the ThreadPool to report events from MailboxProcessor
11.6. The F# MailboxProcessor: 10,000 agents for a game of life
11.7. Summary
12. Parallel workflow and agents programming with TPL DataFlow
12.1. The power of TPL DataFlow
12.2. Designed to compose - TPL DataFlow blocks
12.2.1. Using the BufferBlock<TInput> as a FIFO buffer
12.2.2. Transforming your Data with the TransformBlock<TInput, TOutput>
12.2.3. Completing the work with ActionBlock<TInput >
12.2.4. Linking DataFlow blocks
12.3. Implementing a sophisticated producer/consumer with TDF
12.3.1. A multiple-producer/single-consumer pattern: TPL DataFlow
12.3.2. A single-producer/multiple-consumers pattern: TPL DataFlow
12.4. Enabling agent model in C# using TPL DataFlow
12.4.1. Agent fold-over state and messages: Aggregate
12.4.2. Agents interaction: A parallel word counter
12.5. A parallel workflow to compress and encrypt a large stream
12.5.1. Context: the problem of processing a large Stream of data
12.5.2. Ensuring the order integrity of a stream of messages
12.5.3. Linking, propagating, and completion
12.5.4. Rules for building a DataFlow workflow
12.5.5. Reactive Extensions (Rx) and TPL DataFlow meshes
12.6. Summary
Part 3: Modern patterns of concurrent programming applied
13. Recipes and design patterns for successful concurrent programming
13.1. Asynchronous Object-Pool for recycling objects to reduce memory consumption
13.1.1. Solution: asynchronously recycling a pool of objects
13.2. Custom parallel Fork/Join operator
13.2.1. Solution: composing a pipeline of steps forming the Fork/Join pattern
13.3. Parallelizing Tasks with dependencies - Design your code to optimize performance
13.3.1. Solution: Implementing a dependencies graph of tasks using the F# MailboxProcessor and exposing the methods as standard Task to be consumed also by C#
13.4. Gate for coordinating concurrent I/O operations sharing resources: One write, multiple reads
13.4.1. Solution: Leveraging agent programming model to access and apply multiple read/write operations to shared thread-safe resources
13.5. Thread-Safe Random number generator
13.5.1. Solution: Using the Thread-Local object to guarantee a unique and isolated object instance for each thread accessor
13.6. Polymorphic Event Aggregator
13.6.1. Solution: Implementing a polymorphic publisher-subscriber pattern using the Reactive Extensions Subject type
13.7. Custom Rx Scheduler to control the degree of parallelism
13.7.1. Solution: Implementing a Reactive Extensions scheduler with multiple concurrent agents to control the degree of parallelism
13.8. Concurrent Reactive Scalable Server/Client
13.8.1. Solution: Combining Rx and asynchronous programming
13.9. Reusable custom high-performaning Parallel Filter-Map operator
13.9.1. Solution: Combining filter and map parallel operations
13.10. Non-blocking synchronous message passing model
13.10.1. Solution: Coordinating and balancing the payload between read and write operations using the agent programming model
13.11. Coordinating concurrent jobs using agent programming model
13.11.1. Solution: Implementing an agent that coordinates and concurrently runs a set of jobs with a configured degree of parallelism.
13.12. Composing monadic functions with the Kleisli operator
13.12.1. Solution: Effortlessly combining asynchronous operations using the Kleisli composition operator
13.13. Summary
14. Building a scalable mobile app with concurrent functional programming
14.1. Functional programming on the server in the real world
14.2. How to design a successful performant application
14.2.1. The secret sauce: ACD
14.2.2. A different asynchronous pattern: queuing work for later execution
14.3. Choosing the right concurrent programming model
14.3.1. Real-time communication with SignalR
14.4. Real-time trading: stock market high-level architecture
14.5. Essential elements for the stock market application
14.6. Let’s code the stock market trading application
14.6.1. Benchmark to measure the scalabaility of the Stock-Ticker application
14.7. Summary
Appendixes
Appendix A: Functional programming
A.1. What is Functional Programming?
A.1.1. The benefits of functional programming
A.1.2. The tenets of functional programming
A.1.3. The clash of program paradigms?from imperative to object-oriented to functional programming
A.1.4. Higher-order functions for raising the abstraction
A.1.5. Higher-order functions and lambda expressions for code reusability
A.1.6. Lambda expressions and anonymous functions
A.1.7. Currying
A.1.8. Partially applied functions
A.1.9. Partial application benefits
A.1.10. The power of partial function application and currying in C#
Appendix B: F# overview
B.1. The "let" Binding
B.2. Understanding function signatures in F#
B.3. Create mutable types: mutable and ref
B.4. Functions as first class types
B.5. Composition - Pipe and Composition operators
B.6. Delegates
B.7. Comments
B.8. Open statements
B.9. Basic data types
B.10. Special String definition
B.11. Tuple
B.12. Record-Types
B.13. Discriminated Unions
B.14. Pattern matching
B.15. Active patterns
B.16. Collections
B.17. Arrays
B.18. Sequences (seq)
B.19. Lists
B.20. Sets
B.21. Maps
B.22. Loops
B.23. Class and inheritance
B.24. Abstract classes and inheritance
B.25. Interfaces
B.26. Object expressions
B.27. Casting
B.28. Units of Measure
B.29. Event Module API reference
B.30. Resources
Appendix C: Interoperability between F# asynchronous workflow and .NET Task
About the Technology
Unlock the incredible performance built into your multi-processor machines. Concurrent applications run faster because they spread work across processor cores, performing several tasks at the same time. Modern tools and techniques on the .NET platform, including parallel LINQ, functional programming, asynchronous programming, and the Task Parallel Library, offer powerful alternatives to traditional thread-based concurrency.
About the book
Concurrency in .NET teaches you to write code that delivers the speed you need for performance-sensitive applications. Featuring examples in both C# and F#, this book guides you through concurrent and parallel designs that emphasize functional programming in theory and practice. You’ll start with the foundations of concurrency and master essential techniques and design practices to optimize code running on modern multiprocessor systems.
What's inside
- The most important concurrency abstractions
- Employing the agent programming model
- Implementing real-time event-stream processing
- Executing unbounded asynchronous operations
- Best concurrent practices and patterns that apply to all platforms
About the author
Riccardo Terrell is a seasoned software engineer and Microsoft MVP who is passionate about functional programming. He has over 20 years’ experience delivering cost-effective technology solutions in a competitive business environment.
Riccardo is well known and actively involved in the functional programming community, including .NET meetups and international conferences. He believes in multi-paradigm programming as a mechanism to maximize the power of code. You can keep up with Riccardo and his coding adventures on his blog www.rickyterrell.com.
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Not just for those cutting code on Windows. You can use the gold dust in this book on any platform!
Presents real-world problems and offers different kinds of concurrency to solve them.
Easiest entry into concurrency I’ve come across so far!